Overview

Brought to you by YData

Dataset statistics

Number of variables24
Number of observations100000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory18.3 MiB
Average record size in memory192.0 B

Variable types

Categorical13
Text1
Numeric9
DateTime1

Alerts

article.1 is highly overall correlated with category and 11 other fieldsHigh correlation
category is highly overall correlated with article.1 and 11 other fieldsHigh correlation
cost is highly overall correlated with article.1 and 9 other fieldsHigh correlation
country is highly overall correlated with customer_idHigh correlation
current_price is highly overall correlated with regular_priceHigh correlation
customer_id is highly overall correlated with countryHigh correlation
gender is highly overall correlated with article.1 and 8 other fieldsHigh correlation
productgroup is highly overall correlated with article.1 and 8 other fieldsHigh correlation
regular_price is highly overall correlated with current_priceHigh correlation
rgb_b_main_col is highly overall correlated with article.1 and 9 other fieldsHigh correlation
rgb_b_sec_col is highly overall correlated with article.1 and 9 other fieldsHigh correlation
rgb_g_main_col is highly overall correlated with article.1 and 9 other fieldsHigh correlation
rgb_g_sec_col is highly overall correlated with article.1 and 9 other fieldsHigh correlation
rgb_r_main_col is highly overall correlated with article.1 and 7 other fieldsHigh correlation
rgb_r_sec_col is highly overall correlated with article.1 and 9 other fieldsHigh correlation
sizes is highly overall correlated with article.1 and 6 other fieldsHigh correlation
style is highly overall correlated with article.1 and 6 other fieldsHigh correlation
promo1 is highly imbalanced (66.5%)Imbalance
promo2 is highly imbalanced (95.5%)Imbalance
sizes is highly imbalanced (53.1%)Imbalance
article.1 is uniformly distributedUniform
rgb_b_main_col has 10000 (10.0%) zerosZeros

Reproduction

Analysis started2025-05-08 09:30:14.090401
Analysis finished2025-05-08 09:30:28.615095
Duration14.52 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

country
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.4 KiB
Germany
49400 
Austria
35140 
France
15460 

Length

Max length7
Median length7
Mean length6.8454
Min length6

Characters and Unicode

Total characters684540
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGermany
2nd rowGermany
3rd rowGermany
4th rowGermany
5th rowGermany

Common Values

ValueCountFrequency (%)
Germany 49400
49.4%
Austria 35140
35.1%
France 15460
 
15.5%

Length

2025-05-08T12:30:28.723090image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-08T12:30:28.839441image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
germany 49400
49.4%
austria 35140
35.1%
france 15460
 
15.5%

Most occurring characters

ValueCountFrequency (%)
r 100000
14.6%
a 100000
14.6%
e 64860
9.5%
n 64860
9.5%
G 49400
7.2%
m 49400
7.2%
y 49400
7.2%
A 35140
 
5.1%
u 35140
 
5.1%
s 35140
 
5.1%
Other values (4) 101200
14.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 684540
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 100000
14.6%
a 100000
14.6%
e 64860
9.5%
n 64860
9.5%
G 49400
7.2%
m 49400
7.2%
y 49400
7.2%
A 35140
 
5.1%
u 35140
 
5.1%
s 35140
 
5.1%
Other values (4) 101200
14.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 684540
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 100000
14.6%
a 100000
14.6%
e 64860
9.5%
n 64860
9.5%
G 49400
7.2%
m 49400
7.2%
y 49400
7.2%
A 35140
 
5.1%
u 35140
 
5.1%
s 35140
 
5.1%
Other values (4) 101200
14.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 684540
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 100000
14.6%
a 100000
14.6%
e 64860
9.5%
n 64860
9.5%
G 49400
7.2%
m 49400
7.2%
y 49400
7.2%
A 35140
 
5.1%
u 35140
 
5.1%
s 35140
 
5.1%
Other values (4) 101200
14.8%
Distinct477
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size781.4 KiB
2025-05-08T12:30:29.098364image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters600000
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowYN8639
2nd rowYN8639
3rd rowYN8639
4th rowYN8639
5th rowYN8639
ValueCountFrequency (%)
br3179 610
 
0.6%
mr4948 560
 
0.6%
xg6449 550
 
0.5%
aa7884 540
 
0.5%
op1184 520
 
0.5%
vs6613 510
 
0.5%
qs5396 510
 
0.5%
cb4942 510
 
0.5%
st3419 490
 
0.5%
ze9366 480
 
0.5%
Other values (467) 94720
94.7%
2025-05-08T12:30:29.461756image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8 49480
 
8.2%
6 47430
 
7.9%
7 46260
 
7.7%
2 45710
 
7.6%
4 44500
 
7.4%
1 43690
 
7.3%
3 42760
 
7.1%
9 41580
 
6.9%
5 38590
 
6.4%
X 10380
 
1.7%
Other values (25) 189620
31.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 600000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
8 49480
 
8.2%
6 47430
 
7.9%
7 46260
 
7.7%
2 45710
 
7.6%
4 44500
 
7.4%
1 43690
 
7.3%
3 42760
 
7.1%
9 41580
 
6.9%
5 38590
 
6.4%
X 10380
 
1.7%
Other values (25) 189620
31.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 600000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
8 49480
 
8.2%
6 47430
 
7.9%
7 46260
 
7.7%
2 45710
 
7.6%
4 44500
 
7.4%
1 43690
 
7.3%
3 42760
 
7.1%
9 41580
 
6.9%
5 38590
 
6.4%
X 10380
 
1.7%
Other values (25) 189620
31.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 600000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
8 49480
 
8.2%
6 47430
 
7.9%
7 46260
 
7.7%
2 45710
 
7.6%
4 44500
 
7.4%
1 43690
 
7.3%
3 42760
 
7.1%
9 41580
 
6.9%
5 38590
 
6.4%
X 10380
 
1.7%
Other values (25) 189620
31.6%

sales
Real number (ℝ)

Distinct476
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.7818
Minimum1
Maximum898
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2025-05-08T12:30:29.606178image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q110
median26
Q364
95-th percentile216
Maximum898
Range897
Interquartile range (IQR)54

Descriptive statistics

Standard deviation87.934743
Coefficient of variation (CV)1.5486431
Kurtosis20.657374
Mean56.7818
Median Absolute Deviation (MAD)20
Skewness3.8588957
Sum5678180
Variance7732.5191
MonotonicityNot monotonic
2025-05-08T12:30:29.733225image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 3080
 
3.1%
1 3060
 
3.1%
3 2950
 
2.9%
4 2800
 
2.8%
5 2680
 
2.7%
6 2670
 
2.7%
8 2380
 
2.4%
7 2380
 
2.4%
9 2160
 
2.2%
11 2130
 
2.1%
Other values (466) 73710
73.7%
ValueCountFrequency (%)
1 3060
3.1%
2 3080
3.1%
3 2950
2.9%
4 2800
2.8%
5 2680
2.7%
6 2670
2.7%
7 2380
2.4%
8 2380
2.4%
9 2160
2.2%
10 1940
1.9%
ValueCountFrequency (%)
898 10
< 0.1%
883 10
< 0.1%
881 10
< 0.1%
852 10
< 0.1%
841 10
< 0.1%
827 10
< 0.1%
819 10
< 0.1%
818 20
< 0.1%
797 10
< 0.1%
796 10
< 0.1%

regular_price
Real number (ℝ)

HIGH CORRELATION 

Distinct123
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.3912
Minimum3.95
Maximum197.95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2025-05-08T12:30:29.863570image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum3.95
5-th percentile6.95
Q125.95
median40.95
Q379.95
95-th percentile120.95
Maximum197.95
Range194
Interquartile range (IQR)54

Descriptive statistics

Standard deviation35.272128
Coefficient of variation (CV)0.67324527
Kurtosis0.32235243
Mean52.3912
Median Absolute Deviation (MAD)20
Skewness0.90371157
Sum5239120
Variance1244.123
MonotonicityNot monotonic
2025-05-08T12:30:30.010870image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26.95 3620
 
3.6%
29.95 3160
 
3.2%
30.95 3120
 
3.1%
23.95 3110
 
3.1%
62.95 2690
 
2.7%
25.95 2540
 
2.5%
44.95 2420
 
2.4%
20.95 2330
 
2.3%
3.95 2090
 
2.1%
83.95 1920
 
1.9%
Other values (113) 73000
73.0%
ValueCountFrequency (%)
3.95 2090
2.1%
4.95 570
 
0.6%
5.95 1270
1.3%
6.95 1330
1.3%
7.95 170
 
0.2%
8.95 680
 
0.7%
9.95 510
 
0.5%
10.95 800
 
0.8%
11.95 130
 
0.1%
12.95 190
 
0.2%
ValueCountFrequency (%)
197.95 120
 
0.1%
195.95 160
 
0.2%
153.95 850
0.9%
150.95 150
 
0.1%
141.95 90
 
0.1%
139.95 240
 
0.2%
136.95 200
 
0.2%
135.95 270
 
0.3%
134.95 150
 
0.1%
132.95 490
0.5%

current_price
Real number (ℝ)

HIGH CORRELATION 

Distinct141
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.2908
Minimum1.95
Maximum195.95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2025-05-08T12:30:30.345276image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1.95
5-th percentile3.95
Q111.95
median20.95
Q337.95
95-th percentile74.95
Maximum195.95
Range194
Interquartile range (IQR)26

Descriptive statistics

Standard deviation22.578343
Coefficient of variation (CV)0.79808074
Kurtosis2.9168272
Mean28.2908
Median Absolute Deviation (MAD)11
Skewness1.5474818
Sum2829080
Variance509.78155
MonotonicityNot monotonic
2025-05-08T12:30:30.484733image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.95 3660
 
3.7%
9.95 3360
 
3.4%
11.95 3230
 
3.2%
13.95 3130
 
3.1%
17.95 2930
 
2.9%
12.95 2920
 
2.9%
16.95 2890
 
2.9%
15.95 2720
 
2.7%
7.95 2670
 
2.7%
14.95 2520
 
2.5%
Other values (131) 69970
70.0%
ValueCountFrequency (%)
1.95 1730
1.7%
2.95 1990
2.0%
3.95 1420
 
1.4%
4.95 1650
1.7%
5.95 1960
2.0%
6.95 2160
2.2%
7.95 2670
2.7%
8.95 3660
3.7%
9.95 3360
3.4%
10.95 2400
2.4%
ValueCountFrequency (%)
195.95 10
< 0.1%
178.95 10
< 0.1%
154.95 10
< 0.1%
152.95 20
< 0.1%
145.95 20
< 0.1%
144.95 10
< 0.1%
141.95 10
< 0.1%
140.95 10
< 0.1%
136.95 10
< 0.1%
135.95 10
< 0.1%

ratio
Real number (ℝ)

Distinct2722
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.54564586
Minimum0.29648241
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2025-05-08T12:30:30.619400image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.29648241
5-th percentile0.30283224
Q10.35483871
median0.52504358
Q30.69924812
95-th percentile0.88868275
Maximum1
Range0.70351759
Interquartile range (IQR)0.34440941

Descriptive statistics

Standard deviation0.19436278
Coefficient of variation (CV)0.35620682
Kurtosis-0.9113374
Mean0.54564586
Median Absolute Deviation (MAD)0.17124714
Skewness0.39778993
Sum54564.586
Variance0.03777689
MonotonicityNot monotonic
2025-05-08T12:30:30.756256image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1490
 
1.5%
0.4936708861 1140
 
1.1%
0.3103448276 830
 
0.8%
0.332096475 820
 
0.8%
0.3214862682 760
 
0.8%
0.2988313856 730
 
0.7%
0.746835443 720
 
0.7%
0.3319415449 690
 
0.7%
0.3317422434 570
 
0.6%
0.3548387097 510
 
0.5%
Other values (2712) 91740
91.7%
ValueCountFrequency (%)
0.2964824121 120
 
0.1%
0.298245614 230
 
0.2%
0.2988313856 730
0.7%
0.2991239049 140
 
0.1%
0.2992992993 160
 
0.2%
0.2994161802 40
 
< 0.1%
0.2994996426 310
0.3%
0.2995622264 50
 
0.1%
0.2996108949 60
 
0.1%
0.2996498249 70
 
0.1%
ValueCountFrequency (%)
1 1490
1.5%
0.9920603414 10
 
< 0.1%
0.9917321207 10
 
< 0.1%
0.9915218313 10
 
< 0.1%
0.9904716532 10
 
< 0.1%
0.9899949975 10
 
< 0.1%
0.9890049478 10
 
< 0.1%
0.9880881477 10
 
< 0.1%
0.9857040743 20
 
< 0.1%
0.9841206828 10
 
< 0.1%
Distinct123
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size781.4 KiB
Minimum2014-12-28 00:00:00
Maximum2017-04-30 00:00:00
2025-05-08T12:30:30.895769image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:30:31.036409image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

promo1
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.4 KiB
0
93810 
1
 
6190

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters100000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 93810
93.8%
1 6190
 
6.2%

Length

2025-05-08T12:30:31.162972image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-08T12:30:31.247568image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 93810
93.8%
1 6190
 
6.2%

Most occurring characters

ValueCountFrequency (%)
0 93810
93.8%
1 6190
 
6.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 100000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 93810
93.8%
1 6190
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 100000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 93810
93.8%
1 6190
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 100000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 93810
93.8%
1 6190
 
6.2%

promo2
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.4 KiB
0
99510 
1
 
490

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters100000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 99510
99.5%
1 490
 
0.5%

Length

2025-05-08T12:30:31.339708image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-08T12:30:31.422697image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 99510
99.5%
1 490
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 99510
99.5%
1 490
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 100000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 99510
99.5%
1 490
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 100000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 99510
99.5%
1 490
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 100000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 99510
99.5%
1 490
 
0.5%

customer_id
Real number (ℝ)

HIGH CORRELATION 

Distinct4549
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2721.7265
Minimum1
Maximum5999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2025-05-08T12:30:31.523847image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile203
Q11017
median2091
Q34570.25
95-th percentile5721.05
Maximum5999
Range5998
Interquartile range (IQR)3553.25

Descriptive statistics

Standard deviation1908.0855
Coefficient of variation (CV)0.70105703
Kurtosis-1.4331178
Mean2721.7265
Median Absolute Deviation (MAD)1592
Skewness0.24385097
Sum2.7217265 × 108
Variance3640790.3
MonotonicityNot monotonic
2025-05-08T12:30:31.654885image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1692 80
 
0.1%
1264 80
 
0.1%
1111 80
 
0.1%
1240 70
 
0.1%
22 70
 
0.1%
1726 70
 
0.1%
1586 70
 
0.1%
2328 70
 
0.1%
5890 70
 
0.1%
282 70
 
0.1%
Other values (4539) 99270
99.3%
ValueCountFrequency (%)
1 10
 
< 0.1%
2 10
 
< 0.1%
3 40
< 0.1%
4 30
< 0.1%
5 30
< 0.1%
7 10
 
< 0.1%
8 20
< 0.1%
9 10
 
< 0.1%
10 10
 
< 0.1%
11 30
< 0.1%
ValueCountFrequency (%)
5999 20
< 0.1%
5998 10
 
< 0.1%
5997 10
 
< 0.1%
5996 20
< 0.1%
5995 40
< 0.1%
5994 30
< 0.1%
5992 30
< 0.1%
5991 20
< 0.1%
5990 10
 
< 0.1%
5989 20
< 0.1%

article.1
Categorical

HIGH CORRELATION  UNIFORM 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.4 KiB
OC6355
10000 
AP5568
10000 
CB8861
10000 
LI3529
10000 
GG8661
10000 
Other values (5)
50000 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters600000
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOC6355
2nd rowAP5568
3rd rowCB8861
4th rowLI3529
5th rowGG8661

Common Values

ValueCountFrequency (%)
OC6355 10000
10.0%
AP5568 10000
10.0%
CB8861 10000
10.0%
LI3529 10000
10.0%
GG8661 10000
10.0%
TX1463 10000
10.0%
PC6383 10000
10.0%
VT7698 10000
10.0%
FG2965 10000
10.0%
AC7347 10000
10.0%

Length

2025-05-08T12:30:31.778617image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-08T12:30:31.895941image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
oc6355 10000
10.0%
ap5568 10000
10.0%
cb8861 10000
10.0%
li3529 10000
10.0%
gg8661 10000
10.0%
tx1463 10000
10.0%
pc6383 10000
10.0%
vt7698 10000
10.0%
fg2965 10000
10.0%
ac7347 10000
10.0%

Most occurring characters

ValueCountFrequency (%)
6 90000
15.0%
3 60000
 
10.0%
5 60000
 
10.0%
8 60000
 
10.0%
C 40000
 
6.7%
7 30000
 
5.0%
1 30000
 
5.0%
9 30000
 
5.0%
G 30000
 
5.0%
4 20000
 
3.3%
Other values (11) 150000
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 600000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
6 90000
15.0%
3 60000
 
10.0%
5 60000
 
10.0%
8 60000
 
10.0%
C 40000
 
6.7%
7 30000
 
5.0%
1 30000
 
5.0%
9 30000
 
5.0%
G 30000
 
5.0%
4 20000
 
3.3%
Other values (11) 150000
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 600000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
6 90000
15.0%
3 60000
 
10.0%
5 60000
 
10.0%
8 60000
 
10.0%
C 40000
 
6.7%
7 30000
 
5.0%
1 30000
 
5.0%
9 30000
 
5.0%
G 30000
 
5.0%
4 20000
 
3.3%
Other values (11) 150000
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 600000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
6 90000
15.0%
3 60000
 
10.0%
5 60000
 
10.0%
8 60000
 
10.0%
C 40000
 
6.7%
7 30000
 
5.0%
1 30000
 
5.0%
9 30000
 
5.0%
G 30000
 
5.0%
4 20000
 
3.3%
Other values (11) 150000
25.0%

productgroup
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.4 KiB
SHOES
60000 
HARDWARE ACCESSORIES
20000 
SHORTS
10000 
SWEATSHIRTS
10000 

Length

Max length20
Median length5
Mean length8.7
Min length5

Characters and Unicode

Total characters870000
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSHOES
2nd rowSHORTS
3rd rowHARDWARE ACCESSORIES
4th rowSHOES
5th rowSHOES

Common Values

ValueCountFrequency (%)
SHOES 60000
60.0%
HARDWARE ACCESSORIES 20000
 
20.0%
SHORTS 10000
 
10.0%
SWEATSHIRTS 10000
 
10.0%

Length

2025-05-08T12:30:32.058900image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-08T12:30:32.167981image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
shoes 60000
50.0%
hardware 20000
 
16.7%
accessories 20000
 
16.7%
shorts 10000
 
8.3%
sweatshirts 10000
 
8.3%

Most occurring characters

ValueCountFrequency (%)
S 230000
26.4%
E 130000
14.9%
H 100000
11.5%
O 90000
 
10.3%
R 80000
 
9.2%
A 70000
 
8.0%
C 40000
 
4.6%
W 30000
 
3.4%
I 30000
 
3.4%
T 30000
 
3.4%
Other values (2) 40000
 
4.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 870000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 230000
26.4%
E 130000
14.9%
H 100000
11.5%
O 90000
 
10.3%
R 80000
 
9.2%
A 70000
 
8.0%
C 40000
 
4.6%
W 30000
 
3.4%
I 30000
 
3.4%
T 30000
 
3.4%
Other values (2) 40000
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 870000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 230000
26.4%
E 130000
14.9%
H 100000
11.5%
O 90000
 
10.3%
R 80000
 
9.2%
A 70000
 
8.0%
C 40000
 
4.6%
W 30000
 
3.4%
I 30000
 
3.4%
T 30000
 
3.4%
Other values (2) 40000
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 870000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 230000
26.4%
E 130000
14.9%
H 100000
11.5%
O 90000
 
10.3%
R 80000
 
9.2%
A 70000
 
8.0%
C 40000
 
4.6%
W 30000
 
3.4%
I 30000
 
3.4%
T 30000
 
3.4%
Other values (2) 40000
 
4.6%

category
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.4 KiB
TRAINING
30000 
RUNNING
20000 
FOOTBALL GENERIC
20000 
GOLF
10000 
RELAX CASUAL
10000 

Length

Max length16
Median length10
Mean length9.2
Min length4

Characters and Unicode

Total characters920000
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTRAINING
2nd rowTRAINING
3rd rowGOLF
4th rowRUNNING
5th rowRELAX CASUAL

Common Values

ValueCountFrequency (%)
TRAINING 30000
30.0%
RUNNING 20000
20.0%
FOOTBALL GENERIC 20000
20.0%
GOLF 10000
 
10.0%
RELAX CASUAL 10000
 
10.0%
INDOOR 10000
 
10.0%

Length

2025-05-08T12:30:32.287620image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-08T12:30:32.395732image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
training 30000
23.1%
running 20000
15.4%
football 20000
15.4%
generic 20000
15.4%
golf 10000
 
7.7%
relax 10000
 
7.7%
casual 10000
 
7.7%
indoor 10000
 
7.7%

Most occurring characters

ValueCountFrequency (%)
N 150000
16.3%
I 110000
12.0%
R 90000
9.8%
A 80000
8.7%
G 80000
8.7%
O 70000
7.6%
L 70000
7.6%
E 50000
 
5.4%
T 50000
 
5.4%
F 30000
 
3.3%
Other values (7) 140000
15.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 920000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 150000
16.3%
I 110000
12.0%
R 90000
9.8%
A 80000
8.7%
G 80000
8.7%
O 70000
7.6%
L 70000
7.6%
E 50000
 
5.4%
T 50000
 
5.4%
F 30000
 
3.3%
Other values (7) 140000
15.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 920000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 150000
16.3%
I 110000
12.0%
R 90000
9.8%
A 80000
8.7%
G 80000
8.7%
O 70000
7.6%
L 70000
7.6%
E 50000
 
5.4%
T 50000
 
5.4%
F 30000
 
3.3%
Other values (7) 140000
15.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 920000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 150000
16.3%
I 110000
12.0%
R 90000
9.8%
A 80000
8.7%
G 80000
8.7%
O 70000
7.6%
L 70000
7.6%
E 50000
 
5.4%
T 50000
 
5.4%
F 30000
 
3.3%
Other values (7) 140000
15.2%

cost
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.517
Minimum1.29
Maximum13.29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2025-05-08T12:30:32.503764image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1.29
5-th percentile1.29
Q12.29
median6.95
Q39.6
95-th percentile13.29
Maximum13.29
Range12
Interquartile range (IQR)7.31

Descriptive statistics

Standard deviation3.9147279
Coefficient of variation (CV)0.60069478
Kurtosis-1.2872918
Mean6.517
Median Absolute Deviation (MAD)2.85
Skewness0.099353368
Sum651700
Variance15.325094
MonotonicityNot monotonic
2025-05-08T12:30:32.605452image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
13.29 10000
10.0%
2.29 10000
10.0%
1.7 10000
10.0%
9 10000
10.0%
9.6 10000
10.0%
4.2 10000
10.0%
9.9 10000
10.0%
5.2 10000
10.0%
1.29 10000
10.0%
8.7 10000
10.0%
ValueCountFrequency (%)
1.29 10000
10.0%
1.7 10000
10.0%
2.29 10000
10.0%
4.2 10000
10.0%
5.2 10000
10.0%
8.7 10000
10.0%
9 10000
10.0%
9.6 10000
10.0%
9.9 10000
10.0%
13.29 10000
10.0%
ValueCountFrequency (%)
13.29 10000
10.0%
9.9 10000
10.0%
9.6 10000
10.0%
9 10000
10.0%
8.7 10000
10.0%
5.2 10000
10.0%
4.2 10000
10.0%
2.29 10000
10.0%
1.7 10000
10.0%
1.29 10000
10.0%

style
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.4 KiB
regular
50000 
wide
30000 
slim
20000 

Length

Max length7
Median length5.5
Mean length5.5
Min length4

Characters and Unicode

Total characters550000
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowslim
2nd rowregular
3rd rowregular
4th rowregular
5th rowregular

Common Values

ValueCountFrequency (%)
regular 50000
50.0%
wide 30000
30.0%
slim 20000
 
20.0%

Length

2025-05-08T12:30:32.719419image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-08T12:30:32.813784image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
regular 50000
50.0%
wide 30000
30.0%
slim 20000
 
20.0%

Most occurring characters

ValueCountFrequency (%)
r 100000
18.2%
e 80000
14.5%
l 70000
12.7%
g 50000
9.1%
u 50000
9.1%
a 50000
9.1%
i 50000
9.1%
w 30000
 
5.5%
d 30000
 
5.5%
s 20000
 
3.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 550000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 100000
18.2%
e 80000
14.5%
l 70000
12.7%
g 50000
9.1%
u 50000
9.1%
a 50000
9.1%
i 50000
9.1%
w 30000
 
5.5%
d 30000
 
5.5%
s 20000
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 550000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 100000
18.2%
e 80000
14.5%
l 70000
12.7%
g 50000
9.1%
u 50000
9.1%
a 50000
9.1%
i 50000
9.1%
w 30000
 
5.5%
d 30000
 
5.5%
s 20000
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 550000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 100000
18.2%
e 80000
14.5%
l 70000
12.7%
g 50000
9.1%
u 50000
9.1%
a 50000
9.1%
i 50000
9.1%
w 30000
 
5.5%
d 30000
 
5.5%
s 20000
 
3.6%

sizes
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.4 KiB
xxs,xs,s,m,l,xl,xxl
90000 
xs,s,m,l,xl
10000 

Length

Max length19
Median length19
Mean length18.2
Min length11

Characters and Unicode

Total characters1820000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowxxs,xs,s,m,l,xl,xxl
2nd rowxxs,xs,s,m,l,xl,xxl
3rd rowxxs,xs,s,m,l,xl,xxl
4th rowxxs,xs,s,m,l,xl,xxl
5th rowxxs,xs,s,m,l,xl,xxl

Common Values

ValueCountFrequency (%)
xxs,xs,s,m,l,xl,xxl 90000
90.0%
xs,s,m,l,xl 10000
 
10.0%

Length

2025-05-08T12:30:32.927172image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-08T12:30:33.022485image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
xxs,xs,s,m,l,xl,xxl 90000
90.0%
xs,s,m,l,xl 10000
 
10.0%

Most occurring characters

ValueCountFrequency (%)
, 580000
31.9%
x 560000
30.8%
s 290000
15.9%
l 290000
15.9%
m 100000
 
5.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1820000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
, 580000
31.9%
x 560000
30.8%
s 290000
15.9%
l 290000
15.9%
m 100000
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1820000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
, 580000
31.9%
x 560000
30.8%
s 290000
15.9%
l 290000
15.9%
m 100000
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1820000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
, 580000
31.9%
x 560000
30.8%
s 290000
15.9%
l 290000
15.9%
m 100000
 
5.5%

gender
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.4 KiB
women
70000 
kids
10000 
unisex
10000 
men
10000 

Length

Max length6
Median length5
Mean length4.8
Min length3

Characters and Unicode

Total characters480000
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowwomen
2nd rowwomen
3rd rowwomen
4th rowkids
5th rowwomen

Common Values

ValueCountFrequency (%)
women 70000
70.0%
kids 10000
 
10.0%
unisex 10000
 
10.0%
men 10000
 
10.0%

Length

2025-05-08T12:30:33.131370image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-08T12:30:33.233315image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
women 70000
70.0%
kids 10000
 
10.0%
unisex 10000
 
10.0%
men 10000
 
10.0%

Most occurring characters

ValueCountFrequency (%)
e 90000
18.8%
n 90000
18.8%
m 80000
16.7%
w 70000
14.6%
o 70000
14.6%
i 20000
 
4.2%
s 20000
 
4.2%
k 10000
 
2.1%
d 10000
 
2.1%
u 10000
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 480000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 90000
18.8%
n 90000
18.8%
m 80000
16.7%
w 70000
14.6%
o 70000
14.6%
i 20000
 
4.2%
s 20000
 
4.2%
k 10000
 
2.1%
d 10000
 
2.1%
u 10000
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 480000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 90000
18.8%
n 90000
18.8%
m 80000
16.7%
w 70000
14.6%
o 70000
14.6%
i 20000
 
4.2%
s 20000
 
4.2%
k 10000
 
2.1%
d 10000
 
2.1%
u 10000
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 480000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 90000
18.8%
n 90000
18.8%
m 80000
16.7%
w 70000
14.6%
o 70000
14.6%
i 20000
 
4.2%
s 20000
 
4.2%
k 10000
 
2.1%
d 10000
 
2.1%
u 10000
 
2.1%

rgb_r_main_col
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean161.4
Minimum79
Maximum205
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2025-05-08T12:30:33.329173image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum79
5-th percentile79
Q1138
median160
Q3205
95-th percentile205
Maximum205
Range126
Interquartile range (IQR)67

Descriptive statistics

Standard deviation39.790147
Coefficient of variation (CV)0.24653127
Kurtosis-0.65105527
Mean161.4
Median Absolute Deviation (MAD)26.5
Skewness-0.53681348
Sum16140000
Variance1583.2558
MonotonicityNot monotonic
2025-05-08T12:30:33.427398image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
205 30000
30.0%
139 20000
20.0%
188 10000
 
10.0%
138 10000
 
10.0%
79 10000
 
10.0%
135 10000
 
10.0%
181 10000
 
10.0%
ValueCountFrequency (%)
79 10000
 
10.0%
135 10000
 
10.0%
138 10000
 
10.0%
139 20000
20.0%
181 10000
 
10.0%
188 10000
 
10.0%
205 30000
30.0%
ValueCountFrequency (%)
205 30000
30.0%
188 10000
 
10.0%
181 10000
 
10.0%
139 20000
20.0%
138 10000
 
10.0%
135 10000
 
10.0%
79 10000
 
10.0%

rgb_g_main_col
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean139.6
Minimum26
Maximum238
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2025-05-08T12:30:33.527334image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum26
5-th percentile26
Q1104
median144
Q3181
95-th percentile238
Maximum238
Range212
Interquartile range (IQR)77

Descriptive statistics

Standard deviation63.641814
Coefficient of variation (CV)0.45588692
Kurtosis-0.72863911
Mean139.6
Median Absolute Deviation (MAD)38.5
Skewness-0.41055271
Sum13960000
Variance4050.2805
MonotonicityNot monotonic
2025-05-08T12:30:33.622982image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
104 10000
10.0%
238 10000
10.0%
173 10000
10.0%
140 10000
10.0%
43 10000
10.0%
148 10000
10.0%
26 10000
10.0%
206 10000
10.0%
181 10000
10.0%
137 10000
10.0%
ValueCountFrequency (%)
26 10000
10.0%
43 10000
10.0%
104 10000
10.0%
137 10000
10.0%
140 10000
10.0%
148 10000
10.0%
173 10000
10.0%
181 10000
10.0%
206 10000
10.0%
238 10000
10.0%
ValueCountFrequency (%)
238 10000
10.0%
206 10000
10.0%
181 10000
10.0%
173 10000
10.0%
148 10000
10.0%
140 10000
10.0%
137 10000
10.0%
104 10000
10.0%
43 10000
10.0%
26 10000
10.0%

rgb_b_main_col
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean133.5
Minimum0
Maximum250
Zeros10000
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2025-05-08T12:30:33.722362image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q157
median143
Q3205
95-th percentile250
Maximum250
Range250
Interquartile range (IQR)148

Descriptive statistics

Standard deviation81.148727
Coefficient of variation (CV)0.60785563
Kurtosis-1.2130238
Mean133.5
Median Absolute Deviation (MAD)72.5
Skewness-0.23314943
Sum13350000
Variance6585.1159
MonotonicityNot monotonic
2025-05-08T12:30:33.828238image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
57 10000
10.0%
104 10000
10.0%
0 10000
10.0%
149 10000
10.0%
226 10000
10.0%
205 10000
10.0%
26 10000
10.0%
250 10000
10.0%
181 10000
10.0%
137 10000
10.0%
ValueCountFrequency (%)
0 10000
10.0%
26 10000
10.0%
57 10000
10.0%
104 10000
10.0%
137 10000
10.0%
149 10000
10.0%
181 10000
10.0%
205 10000
10.0%
226 10000
10.0%
250 10000
10.0%
ValueCountFrequency (%)
250 10000
10.0%
226 10000
10.0%
205 10000
10.0%
181 10000
10.0%
149 10000
10.0%
137 10000
10.0%
104 10000
10.0%
57 10000
10.0%
26 10000
10.0%
0 10000
10.0%

rgb_r_sec_col
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.4 KiB
205
40000 
255
30000 
164
30000 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300000
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row255
2nd row255
3rd row255
4th row164
5th row164

Common Values

ValueCountFrequency (%)
205 40000
40.0%
255 30000
30.0%
164 30000
30.0%

Length

2025-05-08T12:30:33.930722image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-08T12:30:34.021960image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
205 40000
40.0%
255 30000
30.0%
164 30000
30.0%

Most occurring characters

ValueCountFrequency (%)
5 100000
33.3%
2 70000
23.3%
0 40000
 
13.3%
1 30000
 
10.0%
6 30000
 
10.0%
4 30000
 
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 300000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 100000
33.3%
2 70000
23.3%
0 40000
 
13.3%
1 30000
 
10.0%
6 30000
 
10.0%
4 30000
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 300000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 100000
33.3%
2 70000
23.3%
0 40000
 
13.3%
1 30000
 
10.0%
6 30000
 
10.0%
4 30000
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 300000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 100000
33.3%
2 70000
23.3%
0 40000
 
13.3%
1 30000
 
10.0%
6 30000
 
10.0%
4 30000
 
10.0%

rgb_g_sec_col
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.4 KiB
155
40000 
187
30000 
211
30000 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row187
2nd row187
3rd row187
4th row211
5th row211

Common Values

ValueCountFrequency (%)
155 40000
40.0%
187 30000
30.0%
211 30000
30.0%

Length

2025-05-08T12:30:34.121665image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-08T12:30:34.210449image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
155 40000
40.0%
187 30000
30.0%
211 30000
30.0%

Most occurring characters

ValueCountFrequency (%)
1 130000
43.3%
5 80000
26.7%
8 30000
 
10.0%
7 30000
 
10.0%
2 30000
 
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 300000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 130000
43.3%
5 80000
26.7%
8 30000
 
10.0%
7 30000
 
10.0%
2 30000
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 300000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 130000
43.3%
5 80000
26.7%
8 30000
 
10.0%
7 30000
 
10.0%
2 30000
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 300000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 130000
43.3%
5 80000
26.7%
8 30000
 
10.0%
7 30000
 
10.0%
2 30000
 
10.0%

rgb_b_sec_col
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.4 KiB
155
40000 
255
30000 
238
30000 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row255
2nd row255
3rd row255
4th row238
5th row238

Common Values

ValueCountFrequency (%)
155 40000
40.0%
255 30000
30.0%
238 30000
30.0%

Length

2025-05-08T12:30:34.313440image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-08T12:30:34.405040image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
155 40000
40.0%
255 30000
30.0%
238 30000
30.0%

Most occurring characters

ValueCountFrequency (%)
5 140000
46.7%
2 60000
20.0%
1 40000
 
13.3%
3 30000
 
10.0%
8 30000
 
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 300000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 140000
46.7%
2 60000
20.0%
1 40000
 
13.3%
3 30000
 
10.0%
8 30000
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 300000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 140000
46.7%
2 60000
20.0%
1 40000
 
13.3%
3 30000
 
10.0%
8 30000
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 300000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 140000
46.7%
2 60000
20.0%
1 40000
 
13.3%
3 30000
 
10.0%
8 30000
 
10.0%

label
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.4 KiB
0
86072 
1
13928 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters100000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 86072
86.1%
1 13928
 
13.9%

Length

2025-05-08T12:30:34.508825image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-08T12:30:34.596097image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 86072
86.1%
1 13928
 
13.9%

Most occurring characters

ValueCountFrequency (%)
0 86072
86.1%
1 13928
 
13.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 100000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 86072
86.1%
1 13928
 
13.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 100000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 86072
86.1%
1 13928
 
13.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 100000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 86072
86.1%
1 13928
 
13.9%

Interactions

2025-05-08T12:30:26.817249image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:30:19.218150image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:30:20.242046image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:30:21.193238image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:30:22.155548image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:30:23.081532image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:30:24.190240image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:30:25.056685image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:30:25.965288image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:30:26.912370image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:30:19.368352image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:30:20.338675image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:30:21.289103image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:30:22.250911image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:30:23.172763image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:30:24.278104image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:30:25.152235image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:30:26.053868image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:30:27.023401image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:30:19.484720image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:30:20.444993image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:30:21.403215image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:30:22.363674image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:30:23.281118image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:30:24.381575image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:30:25.259774image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:30:26.154807image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:30:27.127461image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:30:19.618096image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:30:20.554560image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:30:21.546796image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:30:22.473799image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:30:23.390216image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:30:24.485193image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:30:25.376202image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:30:26.254994image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:30:27.233670image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:30:19.721108image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:30:20.662998image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:30:21.655965image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:30:22.574779image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:30:23.503726image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:30:24.590896image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:30:25.481526image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:30:26.350995image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:30:27.336760image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:30:19.817633image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:30:20.788947image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:30:21.761621image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:30:22.678462image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:30:23.616635image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:30:24.687839image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:30:25.583438image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:30:26.445601image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:30:27.435975image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:30:19.907547image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:30:20.891126image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:30:21.860977image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:30:22.774887image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:30:23.730096image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:30:24.778878image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:30:25.677204image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:30:26.546158image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:30:27.535305image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:30:20.006185image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:30:21.000201image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:30:21.963197image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:30:22.885580image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:30:23.833828image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:30:24.874215image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:30:25.777271image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:30:26.643642image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:30:27.623349image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:30:20.118086image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:30:21.091822image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:30:22.057069image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:30:22.976547image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:30:23.929749image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:30:24.963808image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:30:25.865792image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:30:26.728049image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2025-05-08T12:30:34.680156image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
article.1categorycostcountrycurrent_pricecustomer_idgenderlabelproductgrouppromo1promo2ratioregular_pricergb_b_main_colrgb_b_sec_colrgb_g_main_colrgb_g_sec_colrgb_r_main_colrgb_r_sec_colsalessizesstyle
article.11.0001.0001.0000.0000.0000.0001.0000.0031.0000.0000.0000.0000.0001.0001.0001.0001.0001.0001.0000.0001.0001.000
category1.0001.0000.6990.0000.0000.0000.6900.0050.6380.0000.0000.0000.0000.8370.7950.8200.7950.6740.7950.0000.6670.589
cost1.0000.6991.0000.0000.0000.0000.7240.0000.8160.0000.0000.0000.000-0.0910.782-0.8180.7820.0120.7820.0001.0000.876
country0.0000.0000.0001.0000.0760.9180.0000.0100.0000.0080.1640.0430.1750.0000.0000.0000.0000.0000.0000.0260.0000.000
current_price0.0000.0000.0000.0761.0000.0000.0000.1810.0000.0690.0290.3720.8850.0000.0000.0000.0000.0000.000-0.1780.0000.000
customer_id0.0000.0000.0000.9180.0001.0000.0000.0160.0000.0180.1440.009-0.0000.0000.0000.0000.0000.0000.0000.0040.0000.000
gender1.0000.6900.7240.0000.0000.0001.0000.0000.3090.0000.0000.0000.0000.8160.5460.5770.5460.4450.5460.0001.0000.483
label0.0030.0050.0000.0100.1810.0160.0001.0000.0000.0640.0200.4620.0220.0000.0000.0050.0000.0000.0000.0990.0000.000
productgroup1.0000.6380.8160.0000.0000.0000.3090.0001.0000.0000.0000.0000.0001.0000.5530.8610.5530.7760.5530.0000.2720.494
promo10.0000.0000.0000.0080.0690.0180.0000.0640.0001.0000.0470.1620.0160.0000.0000.0000.0000.0000.0000.1260.0000.000
promo20.0000.0000.0000.1640.0290.1440.0000.0200.0000.0471.0000.0440.0280.0000.0000.0000.0000.0000.0000.0160.0000.000
ratio0.0000.0000.0000.0430.3720.0090.0000.4620.0000.1620.0441.000-0.0710.0000.0000.0000.0000.0000.000-0.4350.0000.000
regular_price0.0000.0000.0000.1750.885-0.0000.0000.0220.0000.0160.028-0.0711.0000.0000.0000.0000.0000.0000.0000.0110.0000.000
rgb_b_main_col1.0000.837-0.0910.0000.0000.0000.8160.0001.0000.0000.0000.0000.0001.0000.8420.2120.842-0.6890.8420.0001.0000.931
rgb_b_sec_col1.0000.7950.7820.0000.0000.0000.5460.0000.5530.0000.0000.0000.0000.8421.0000.8121.0000.6431.0000.0000.4080.400
rgb_g_main_col1.0000.820-0.8180.0000.0000.0000.5770.0050.8610.0000.0000.0000.0000.2120.8121.0000.8120.0370.8120.0000.6670.830
rgb_g_sec_col1.0000.7950.7820.0000.0000.0000.5460.0000.5530.0000.0000.0000.0000.8421.0000.8121.0000.6431.0000.0000.4080.400
rgb_r_main_col1.0000.6740.0120.0000.0000.0000.4450.0000.7760.0000.0000.0000.000-0.6890.6430.0370.6431.0000.6430.0000.4080.570
rgb_r_sec_col1.0000.7950.7820.0000.0000.0000.5460.0000.5530.0000.0000.0000.0000.8421.0000.8121.0000.6431.0000.0000.4080.400
sales0.0000.0000.0000.026-0.1780.0040.0000.0990.0000.1260.016-0.4350.0110.0000.0000.0000.0000.0000.0001.0000.0000.000
sizes1.0000.6671.0000.0000.0000.0001.0000.0000.2720.0000.0000.0000.0001.0000.4080.6670.4080.4080.4080.0001.0000.509
style1.0000.5890.8760.0000.0000.0000.4830.0000.4940.0000.0000.0000.0000.9310.4000.8300.4000.5700.4000.0000.5091.000

Missing values

2025-05-08T12:30:27.802020image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-08T12:30:28.225947image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

countryarticlesalesregular_pricecurrent_priceratioretailweekpromo1promo2customer_idarticle.1productgroupcategorycoststylesizesgenderrgb_r_main_colrgb_g_main_colrgb_b_main_colrgb_r_sec_colrgb_g_sec_colrgb_b_sec_collabel
0GermanyYN8639285.953.950.6638662016-03-27001003.0OC6355SHOESTRAINING13.29slimxxs,xs,s,m,l,xl,xxlwomen205104572551872550
1GermanyYN8639285.953.950.6638662016-03-27001003.0AP5568SHORTSTRAINING2.29regularxxs,xs,s,m,l,xl,xxlwomen1882381042551872550
2GermanyYN8639285.953.950.6638662016-03-27001003.0CB8861HARDWARE ACCESSORIESGOLF1.70regularxxs,xs,s,m,l,xl,xxlwomen20517302551872550
3GermanyYN8639285.953.950.6638662016-03-27001003.0LI3529SHOESRUNNING9.00regularxxs,xs,s,m,l,xl,xxlkids2051401491642112380
4GermanyYN8639285.953.950.6638662016-03-27001003.0GG8661SHOESRELAX CASUAL9.60regularxxs,xs,s,m,l,xl,xxlwomen138432261642112380
5GermanyYN8639285.953.950.6638662016-03-27001003.0TX1463SWEATSHIRTSTRAINING4.20widexxs,xs,s,m,l,xl,xxlwomen791482051642112381
6GermanyYN8639285.953.950.6638662016-03-27001003.0PC6383SHOESFOOTBALL GENERIC9.90widexs,s,m,l,xlunisex13926262051551550
7GermanyYN8639285.953.950.6638662016-03-27001003.0VT7698SHOESINDOOR5.20widexxs,xs,s,m,l,xl,xxlwomen1352062502051551551
8GermanyYN8639285.953.950.6638662016-03-27001003.0FG2965HARDWARE ACCESSORIESRUNNING1.29slimxxs,xs,s,m,l,xl,xxlwomen1811811812051551550
9GermanyYN8639285.953.950.6638662016-03-27001003.0AC7347SHOESFOOTBALL GENERIC8.70regularxxs,xs,s,m,l,xl,xxlmen1391371372051551551
countryarticlesalesregular_pricecurrent_priceratioretailweekpromo1promo2customer_idarticle.1productgroupcategorycoststylesizesgenderrgb_r_main_colrgb_g_main_colrgb_b_main_colrgb_r_sec_colrgb_g_sec_colrgb_b_sec_collabel
99990GermanyPW627822757.9526.950.4650562016-06-26001489.0OC6355SHOESTRAINING13.29slimxxs,xs,s,m,l,xl,xxlwomen205104572551872550
99991GermanyPW627822757.9526.950.4650562016-06-26001489.0AP5568SHORTSTRAINING2.29regularxxs,xs,s,m,l,xl,xxlwomen1882381042551872550
99992GermanyPW627822757.9526.950.4650562016-06-26001489.0CB8861HARDWARE ACCESSORIESGOLF1.70regularxxs,xs,s,m,l,xl,xxlwomen20517302551872550
99993GermanyPW627822757.9526.950.4650562016-06-26001489.0LI3529SHOESRUNNING9.00regularxxs,xs,s,m,l,xl,xxlkids2051401491642112380
99994GermanyPW627822757.9526.950.4650562016-06-26001489.0GG8661SHOESRELAX CASUAL9.60regularxxs,xs,s,m,l,xl,xxlwomen138432261642112380
99995GermanyPW627822757.9526.950.4650562016-06-26001489.0TX1463SWEATSHIRTSTRAINING4.20widexxs,xs,s,m,l,xl,xxlwomen791482051642112380
99996GermanyPW627822757.9526.950.4650562016-06-26001489.0PC6383SHOESFOOTBALL GENERIC9.90widexs,s,m,l,xlunisex13926262051551550
99997GermanyPW627822757.9526.950.4650562016-06-26001489.0VT7698SHOESINDOOR5.20widexxs,xs,s,m,l,xl,xxlwomen1352062502051551550
99998GermanyPW627822757.9526.950.4650562016-06-26001489.0FG2965HARDWARE ACCESSORIESRUNNING1.29slimxxs,xs,s,m,l,xl,xxlwomen1811811812051551550
99999GermanyPW627822757.9526.950.4650562016-06-26001489.0AC7347SHOESFOOTBALL GENERIC8.70regularxxs,xs,s,m,l,xl,xxlmen1391371372051551550